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• W1569655584 abstract "Online word-of-mouth communication in the form of product reviews is a major information source for consumers and marketers about product quality. The literature has used the mean of online reviews to predict product sales, assuming that the mean reflects product quality. However, using a combination of econometric, experimental, and analytical results, we show that the mean is a biased estimator of product quality due to two self-selection biases (purchasing and under-reporting bias). First, econometric results with secondary data from Amazon.com show that almost all products have an asymmetric bimodal (J-shaped) distribution with more positive than negative reviews. Second, experimental results where all respondents wrote reviews show that their reviews have an approximately normal distribution with roughly equal number of positive and negative reviews. This implies two biases: (1) purchasing bias - only consumers with favorable disposition towards a product purchase the product and have the opportunity to write a product review, and (2) under-reporting bias - consumers with polarized (either positive or negative) reviews are more likely to report their reviews than consumers with moderate reviews. This results in a J-shaped distribution of online product reviews that renders the mean a biased estimator of product quality. Third, we develop an analytical model to derive the conditions for the mean to become an unbiased estimator of product quality. Based on these conditions, we build a new model that integrates three distributional parameters - mean, standard deviation, and the two modes of the online product reviews (to overcome under-reporting bias) and product price (to overcome purchasing bias). This model is shown to be a superior predictive model of future product sales compared to competing models." @default.
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• W1569655584 date "2007-01-01" @default.
• W1569655584 modified "2023-09-23" @default.
• W1569655584 title "Why Do Online Product Reviews Have a J-Shaped Distribution? Overcoming Biases in Online Word-of-Mouth Communication" @default.
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• W1569655584 doi "https://doi.org/10.2139/ssrn.2380298" @default.
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